Distribution network resource scheduling method, device and equipment

Abstract

The embodiment of the invention provides a power distribution network resource scheduling method, device and equipment. The method comprises the following steps: obtaining schedulable resource information of a power distribution network; based on the schedulable resource information, a two-stage model is constructed, and the two-stage model comprises a preventive network reconstruction model of a prevention stage and an optimization model of an emergency stage; on the basis of minimization of the dispatching cost of the power distribution network in the prevention stage and minimization of the load switching cost of the power distribution network on the premise of maximization of the negative influence degree of extreme events in the emergency stage, nesting solution is carried out on a preventive network reconstruction model and an optimization model; obtaining a first scheduling strategy of a prevention stage and a second scheduling strategy of an emergency stage for the schedulable resources; and scheduling the schedulable resources according to the first scheduling strategy of the prevention stage or the second scheduling strategy of the emergency stage. The method is used for achieving the technical effect of improving the power distribution network resource scheduling precision.

Description

Technical Field

[0001] This application relates to the field of power technology, and in particular, to a method, device, and equipment for scheduling distribution network resources. Background Art

[0002] To cope with catastrophic extreme events that the distribution network may encounter, by adjusting the scheduling strategies of the schedulable resources in the distribution network, the losses of the power system are reduced.

[0003] Related technologies adjust the scheduling strategies of the distribution network in different stages according to pre-disaster prevention strategies or disaster emergency strategies, and the pre-disaster prevention strategies and disaster emergency strategies are disjoint.

[0004] However, the operating states of the schedulable resources in the distribution network in the pre-disaster prevention and disaster emergency stages are interrelated. Therefore, using a scheduling strategy that does not consider the correlation between the two stages to schedule the scheduling resources has the technical problems of low scheduling accuracy, resulting in large losses caused by disasters to the distribution network. Summary of the Invention

[0005] Embodiments of this application provide a method, device, and equipment for scheduling distribution network resources to achieve the technical effect of improving the scheduling accuracy of distribution network resources.

[0006] In a first aspect, embodiments of this application provide a method for scheduling distribution network resources, including:

[0007] Obtain the schedulable resource information of the distribution network;

[0008] Based on the schedulable resource information, construct a two-stage model, where the two-stage model includes a preventive network reconstruction model in the prevention stage and an optimization model in the emergency stage; wherein, the optimization model in the emergency stage includes an extreme event model and an emergency network reconstruction model;

[0009] Based on minimizing the scheduling cost of the distribution network in the prevention stage and minimizing the load switching cost of the distribution network on the premise of maximizing the negative impact degree of extreme events in the emergency stage, perform nested solution on the preventive network reconstruction model and the optimization model to obtain the first scheduling strategy in the prevention stage and the second scheduling strategy in the emergency stage for the schedulable resources;

[0010] Schedule the schedulable resources according to the first scheduling strategy in the prevention stage or the second scheduling strategy in the emergency stage.

[0011] In a possible implementation manner, on the premise of minimizing the scheduling cost of the distribution network in the prevention stage, maximizing the negative impact degree of extreme events in the emergency stage, and minimizing the load switching cost of the distribution network, the preventive network reconstruction model and the optimization model are solved by nesting to obtain the first scheduling strategy in the prevention stage and the second scheduling strategy in the emergency stage for the dispatchable resources, including:

[0012] Based on minimizing the scheduling cost of the distribution network in the prevention stage, according to the dispatchable resource information, determine the first objective function in the prevention stage; wherein, the first objective function includes the output constraints of the dispatchable resources determined according to the dispatchable resource information.

[0013] In a possible implementation manner, on the premise of minimizing the scheduling cost of the distribution network in the prevention stage, maximizing the negative impact degree of extreme events in the emergency stage, and minimizing the load switching cost of the distribution network, the preventive network reconstruction model and the optimization model are solved by nesting to obtain the first scheduling strategy in the prevention stage and the second scheduling strategy in the emergency stage for the dispatchable resources, further including:

[0014] Based on maximizing the negative impact degree of extreme events, determine the uncertainty constraints regarding the extreme events according to the dispatchable resource information;

[0015] Based on minimizing the load switching cost of the distribution network in the emergency stage, according to the dispatchable resource information, determine the second objective function in the emergency stage; the second objective function includes the uncertainty constraints.

[0016] In a possible implementation manner, the uncertainty constraints include the constraints of power line outages caused by extreme events.

[0017] In a possible implementation manner, on the premise of minimizing the scheduling cost of the distribution network in the prevention stage, maximizing the negative impact degree of extreme events in the emergency stage, and minimizing the load switching cost of the distribution network, the preventive network reconstruction model and the optimization model are solved by nesting to obtain the first scheduling strategy in the prevention stage and the second scheduling strategy in the emergency stage for the dispatchable resources, further including:

[0018] Determine the master problem according to the first objective function in the prevention stage;

[0019] Determine the sub-problem according to the second objective function in the emergency stage;

[0020] Based on the nested solution algorithm, the master problem and the sub-problem are solved nestedly to obtain the first scheduling strategy for the preventive stage and the second scheduling strategy for the emergency stage of the schedulable resources.

[0021] In a possible implementation manner, the step of solving the master problem and the sub-problem nestedly based on the nested solution algorithm to obtain the first scheduling strategy for the preventive stage and the second scheduling strategy for the emergency stage of the schedulable resources includes:

[0022] Repeat the following steps until a preset condition is met:

[0023] Solve the master problem to obtain the first scheduling result for the schedulable resources;

[0024] In response to the first scheduling result not meeting the preset condition, substitute the first scheduling result into the sub-problem and solve the sub-problem to obtain the second scheduling result for the schedulable resources;

[0025] Based on the first scheduling result and the second scheduling result, adjust the parameters of the master problem and the sub-problem respectively to obtain the updated master problem and sub-problem; where

[0026] Judge whether the preset condition is met according to the first scheduling result.

[0027] In a possible implementation manner, the schedulable resource information includes at least one of the source-network-load-storage information of the following schedulable resources: new energy units, conventional units, transmission line switches, and load shedding devices.

[0028] In a second aspect, an embodiment of the present application provides a distribution network resource scheduling device, including:

[0029] An acquisition module, configured to acquire the schedulable resource information of the distribution network;

[0030] A construction module, configured to construct a two-stage model based on the schedulable resource information, where the two-stage model includes a preventive network reconstruction model in the preventive stage and an optimization model in the emergency stage; where the optimization model in the emergency stage includes an extreme event model and an emergency network reconstruction model;

[0031] A solution module, configured to perform nested solution on the preventive network reconstruction model and the optimization model based on minimizing the scheduling cost of the distribution network in the preventive stage and minimizing the load switching cost of the distribution network on the premise of maximizing the negative impact degree of extreme events in the emergency stage, to obtain the first scheduling strategy for the preventive stage and the second scheduling strategy for the emergency stage of the schedulable resources;

[0032] A scheduling module, configured to schedule the schedulable resources according to the first scheduling policy in the prevention stage or the second scheduling policy in the emergency stage.

[0033] In a third aspect, an embodiment of the present application provides a power distribution network resource scheduling device, including: a memory and a processor;

[0034] The memory stores computer-executable instructions;

[0035] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementation manners of the first aspect.

[0036] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the above first aspect and / or various possible implementation manners of the first aspect.

[0037] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the above first aspect and / or various possible implementation manners of the first aspect.

[0038] The power distribution network resource scheduling method, device and equipment provided by the embodiments of the present application obtain the schedulable resource information of the power distribution network, and based on the schedulable resource information, construct a two-stage model of the prevention stage and the emergency stage; on the premise of minimizing the scheduling cost of the power distribution network in the prevention stage and minimizing the load switching cost of the power distribution network under the premise of maximizing the negative impact degree of extreme events in the emergency stage, nested solutions are performed on the preventive network reconstruction model and the optimization model to obtain the scheduling strategies for the schedulable resources in the prevention stage and the emergency stage respectively. By performing two-stage modeling and nested solutions on the two-stage models, the scheduling strategies of the two stages are associated, thereby improving the scheduling accuracy of the power distribution network scheduling resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0040] Figure 1 It is a schematic flowchart of the power distribution network resource scheduling method provided by the present application;

[0041] Figure 2 It is a schematic structural diagram of the power distribution network resource scheduling device provided by the present application;

[0042] Figure 3 It is a schematic structural diagram of the power distribution network resource scheduling device provided by the present application.

[0043] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0044] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0045] The economic dispatch problem of the power system refers to determining the active output plan of each generator set in operation to achieve the optimal target under the premise of satisfying the system power balance and related operating constraints. The dispatch target is usually determined according to actual needs, such as the dispatch target can be the lowest input cost. When the distribution network of the power system is affected by catastrophic extreme events such as hurricanes and ice disasters, it is necessary to adjust the dispatch strategy of dispatchable resources such as generator sets in the distribution network to ensure the safe operation of the distribution network, thereby reducing the losses of the power system.

[0046] The relevant technology adjusts the dispatching strategy of the distribution network at different stages according to the pre-disaster prevention strategy or the disaster emergency strategy. The pre-disaster prevention strategy and the disaster emergency strategy are separated from each other. In the pre-disaster prevention stage, the power system's ability to withstand extreme weather is enhanced by identifying and reinforcing the key components of the distribution network before extreme events, and strengthening or changing the topology of the power grid. In the disaster emergency stage, when extreme events occur, emergency control of generator sets and lines is performed to isolate the parts of the power grid that may become unstable or suffer severe shocks from the remaining stable parts of the power grid, thereby reducing the scope of the fault.

[0047] However, the operating status of the dispatchable resources in the distribution network in the two stages of pre-disaster prevention and disaster emergency response is interrelated. Therefore, using a dispatch strategy that does not consider the correlation between the two stages to dispatch the dispatchable resources has the problem of low dispatch accuracy, which leads to large losses to the distribution network caused by disasters.

[0048] The distribution network resource scheduling method, device and equipment provided in this application are used to solve the above-mentioned technical problems.

[0049] The following uses specific embodiments to elaborate in detail on the technical solution of the present application and how the technical solution of the present application solves the above technical problems. The following several specific embodiments can be combined with each other, and the same or similar concepts or processes may not be elaborated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0050] Figure 1 It is a schematic flowchart of the distribution network resource scheduling method provided by the present application. As Figure 1 shown, the method includes:

[0051] S101. Obtain the schedulable resource information of the distribution network.

[0052] S102. Based on the schedulable resource information, construct a two-stage model, where the two-stage model includes a preventive network reconstruction model in the preventive stage and an optimization model in the emergency stage; among them, the optimization model in the emergency stage includes an extreme event model and an emergency network reconstruction model.

[0053] It should be noted that the model constructed in the preventive stage provides a preventive distribution network resource scheduling strategy for coping with complex extreme events, and the model constructed in the emergency stage provides an emergency distribution network resource scheduling strategy when encountering complex extreme events.

[0054] S103. Based on minimizing the scheduling cost of the distribution network in the preventive stage and minimizing the load switching cost of the distribution network on the premise of maximizing the negative impact degree of extreme events in the emergency stage, perform nested solution on the preventive network reconstruction model and the optimization model to obtain the first scheduling strategy in the preventive stage and the second scheduling strategy in the emergency stage for the schedulable resources.

[0055] It should be noted that through the nested solution of the preventive network reconstruction model and the optimization model, the scheduling strategy of the schedulable resources of the distribution network in the emergency stage is associated with the scheduling state of the schedulable resources of the distribution network in the preventive stage, so that the operation state switching of the schedulable resources in different stages is more coordinated.

[0056] S104. Schedule the schedulable resources according to the first scheduling strategy in the preventive stage or the second scheduling strategy in the emergency stage.

[0057] Exemplarily, in the preventive stage before the occurrence of extreme events, determine the specific output (including electric power) magnitude and output time of the schedulable resources according to the first scheduling strategy, and the control center of the distribution network controls the schedulable resources to output according to the above output magnitude; when extreme events occur, determine the specific output (including electric power) magnitude and output time of the schedulable resources when extreme events occur according to the second scheduling strategy, and the control center of the distribution network controls the schedulable resources to output according to the above output magnitude.

[0058] The power distribution network resource scheduling method provided by the embodiments of this application obtains the schedulable resource information of the power distribution network, and constructs a two-stage model for the prevention stage and the emergency stage based on the schedulable resource information; on the premise of minimizing the scheduling cost of the power distribution network in the prevention stage and minimizing the load switching cost of the power distribution network while maximizing the negative impact degree of extreme events in the emergency stage, the preventive network reconstruction model and the optimization model are nested and solved to obtain the scheduling strategies for the schedulable resources in the prevention stage and the emergency stage respectively. By performing two-stage modeling and nested solving of the two-stage models, the scheduling strategies for the two stages are associated, thereby improving the scheduling accuracy of the power distribution network scheduling resources.

[0059] Taking the power distribution network's response to extreme events such as hurricane weather as an example, the technical solution of this application will be described in more detail below.

[0060] Considering that the power distribution network will be affected by catastrophic extreme events, and the impact brought by catastrophic extreme events is uncertain, therefore, uncertainty theory can be used for modeling.

[0061] Robust optimization is a class of mathematical problems that consider parameter uncertainty factors, aiming to handle the challenges of parameter uncertainty in optimization problems, and is committed to optimizing the solution in the worst case, or the solution under the worst parameter distribution, so that the finally obtained solution has a very strong ability to resist uncertainty or risk.

[0062] In a two-stage robust optimization problem, there are generally two different levels of decision variables, also known as the decision variables in the first stage and the decision variables in the second stage. The parameters related to the decision in the first stage are determined, and the decision in the first stage needs to be made first; the parameters related to the decision in the second stage include some uncertain parameters, and its decision can only be obtained after the decision in the first stage is determined and the parameters in the second stage are determined.

[0063] Two-stage robust optimization modeling jointly optimizes the decisions of the two stages while considering the uncertainty of the parameters in the second stage. Therefore, the mathematical expression of the constructed two-stage model can be the following formula (1):

[0064]

[0065] In the formula, \(x'_1\) represents the decision variable in the first stage; \(x'_2\) represents the decision variable in the second stage; \(c'_1\) represents the parameter related to the decision variable in the first stage, which is determined; \(c'_2\) is the parameter related to the decision variable in the second stage, which is uncertain, and the value range of its uncertain parameter is characterized by the uncertainty set \(\eta'\); the min function related to \(x'_1\) is the minimization function related to the parameter \(x'_1\), which is the objective function corresponding to the strategy in the first stage, and the strategy of this part needs to be determined first; the max function related to \(u'\) is the maximization function related to the parameter \(u'\); the min function related to \(x'_2\) is the minimization function related to the parameter \(x'_2\), which is the objective function corresponding to the strategy in the second stage, and the strategy of this part needs to be made after the strategy in the first stage is determined; \(A'\) represents the constraint matrix in the first stage; \(d'\) represents the constraint vector, which together with \(A'\) defines the decision variable in the first stage; \(G'\) represents the constraint matrix in the second stage; \(h'\) represents the constraint vector, which together with \(G'\) defines the decision variable in the second stage; \(E'\) represents the coupling matrix, which is used to connect the decision variable \(x'_1\) in the first stage and the constraint conditions in the second stage; \(M'\) represents the coefficient matrix, which is used to represent the influence of uncertainty factors on the constraints in the second stage. represents an \(n\times1\) - dimensional non - negative continuous decision variable; represents an \(m\times1\) - dimensional non - negative continuous decision variable.

[0066] Therefore, modeling can be carried out based on the two - stage robust optimization algorithm.

[0067] In some specific embodiments, the first stage corresponds to the prevention stage of the technical solution of the present application; the second stage corresponds to the emergency stage of the technical solution of the present application. In extreme cases, the defense - emergency coordination decision - making process of the distribution network system can be modeled as a two - stage three - layer optimization problem. The prevention stage is a minimum - value problem of solving the optimal preventive network reconfiguration strategy of the system, and at the same time becomes the outer layer; the second stage includes two layers, namely the middle layer and the inner layer. The middle layer simulates the disturbance behavior of the distribution network under extreme events, which is a maximum - value problem of obtaining the worst - case line - fault scenario; the inner layer is another minimum - value problem of obtaining the optimal emergency network reconfiguration strategy. Therefore, this two - stage three - layer optimization problem can be written in the compact form of formula (2) as follows:

[0068]

[0069] In the formula, \(x_1\) represents the decision variable in the prevention stage; \(x_2\) represents the decision variable in the emergency stage; represents the parameter related to the decision variable in the prevention stage; represents the parameter related to the decision variable in the emergency stage; Ω1 and Ω2Denote the sets of decision variables for the prevention stage and the emergency stage respectively; \(u\) represents the uncertainty parameter; \(\eta\) represents the uncertainty set; \(A\) represents the constraint matrix for the prevention stage; \(a\) represents the constraint vector, which together with \(A\) defines the decision variables for the prevention stage; \(Z\) w represents the uncertainty of wind power and photovoltaic power under the prevention stage; and represent the benchmark values of the uncertainty parameters, which are used to characterize the worst and best impacts of the uncertainty parameters on the system; \(z\) + w and \(z\) - w are 0-1 variables, which are used to represent the fluctuations of the wind and light output; \(B\) W represents the constraint coefficient matrix related to the uncertainty of the wind and light output; \(\Gamma\) w represents the uncertainty budget parameter of the wind and light output; \(\Gamma\) l represents the uncertainty budget parameter related to the line; \(z\) l represents the 0-1 variable of the transmission line fault outage; \(H\) represents the coefficient matrix of the constraints that need to be satisfied for the emergency stage decision-making; \(E\) represents the coupling matrix coefficient, which reflects the influence of the prevention stage decision on the emergency stage constraints; \(F\) represents the matrix coefficient of the constraint matrix coefficient with the wind and light uncertainty parameters; \(I\) represents the constraint matrix coefficient with the line uncertainty parameters; \(d\) represents the constant matrix.

[0070] Before the extreme event affects the power grid, preventive network reconfiguration, as the first line of defense of the power system, aims to reconstruct the power system resources in advance, adjust the system operation state, actively respond to the upcoming event, and avoid a large number of power outages caused by insufficient system preparation. In the preventive network reconfiguration stage, a deterministic reconfiguration method can be used to calculate the processing prediction values of each dispatchable resource. The dispatchable resources in the prevention stage mainly include: new energy units, conventional units, transmission line switches, and preventive load shedding. The obtained dispatchable resource information includes the source-network-load-storage information of the above dispatchable resources. By dispatching various adjustable resources of the power system and network reconfiguration, operating space is reserved for subsequent severe operation scenarios, thereby reducing the emergency load shedding losses caused by extreme disturbance scenarios.

[0071] In some implementation manners of these embodiments, in the prevention stage, on the premise of minimizing the dispatching cost of the distribution network in the prevention stage and maximizing the negative impact degree of the extreme event in the emergency stage, the load switching cost of the distribution network is minimized, and the preventive network reconfiguration model and the optimization model are nested and solved to obtain the first dispatching strategy for the prevention stage and the second dispatching strategy for the emergency stage of the dispatchable resources, including:

[0072] Based on minimizing the dispatching cost of the distribution network in the prevention stage, according to the dispatchable resource information, the first objective function in the prevention stage is determined; among them, the first objective function includes the output constraints of the dispatchable resources determined according to the dispatchable resource information.

[0073] Specifically, since the middle and inner layer models consider the robustness of system operation, the first objective function in the outer prevention stage is to minimize the dispatching cost, including the unit operation cost of the units, the reserve cost, the penalty cost for wind and photovoltaic curtailment, the cost of preventive load shedding losses, and the switching cost. Based on this, the first objective function can be expressed as the following formula (3):

[0074]

[0075] In the formula, T Pre is the adjustment time reserved for the preventive network reconstruction stage; G, N, W, PV, and L are the sets of conventional units, network nodes, wind power, photovoltaic units, and network branches respectively; P g,t represents the output of the conventional units in the prevention stage; and represent the upward and downward reserve capacities of the units respectively; is the curtailed wind and photovoltaic power in the prevention stage; is the load shedding amount of node j in the prevention stage; C g is the unit output cost of the units in the prevention stage, and are the upward and downward reserve costs of the units in the prevention stage respectively; is the unit wind curtailment cost in the prevention stage; is the unit photovoltaic curtailment cost in the prevention stage; C Pre,load represents the unit load curtailment cost in the prevention stage; C switch represents the switching cost in the prevention stage; z ij is the 0-1 variable of the line switch in the prevention stage, and z ij,0 is the initial state of the line in the prevention stage.

[0076] The output constraints of the dispatchable resources determined according to the dispatchable resource information can include the first linearized DistFlow (full name: Dstribution Flow, which is a mathematical model for power system distribution network analysis and optimization) type constraints and the first other constraints.

[0077] Regarding the first linearized DistFlow branch model constraints, this branch model can be deployed in the distribution network with a radial topology, can incorporate reactive power and power amplitude into the model, and is suitable for power flow analysis of the distribution network. The first linearized DistFlow branch model constraints can be expressed as the following formulas (4)-(8):

[0078]

[0079] In the formula, respectively represent the conventional unit output, wind-solar power plant output, and load power of node j within a single period of the prevention stage. G j , W j , PV j are respectively the sets of conventional units, wind power, and photovoltaic units on node j; and, are respectively the active power and reactive power flowing through branch ij at time t; is the reactance of branch ij; and are respectively the active power and reactive power of the load injected into node j at time t; respectively represent the conventional unit output, wind-solar power plant output, and reactive power of the load at node j within a single period of the prevention stage; is the reactive power shedding amount of node j in the prevention stage; and are respectively the charging power and discharging power of the energy storage at node j at time t; and are respectively the voltage amplitudes of node i and node j; V 0 is the voltage of the slack node; The M function is the big M method function; f(j) and s(j) are respectively the sets of the parent node and child node of node j in the distribution system. The parent node and child node are respectively the upstream node and downstream node of the power flow transmission in the distribution system branch; r ij is the resistance of branch ij; t P ij is the lower limit of the reactive power of branch ij; t Q ij is the lower limit of the reactive power of branch ij; V i is the lower limit of the voltage of node voltage i; is the upper limit of the active power of branch ij; is the upper limit of the reactive power of branch ij; z i,tj is the 0-1 variable of the line switch at time t in the prevention stage.

[0080] Regarding the first other constraints, including the conventional unit output and ramping constraints, such as Formulas (9) to (11), the load shedding constraints, such as Formula (12), the wind-solar output constraints, such as Formulas (13)-(14), the energy storage constraints, such as Formulas (15)-(18), and the distribution system connectivity and radiation constraints, such as Formulas (19) to (22):

[0081]

[0082]

[0083] β ij +β ji =z ij (20);

[0084]

[0085] β 1j =0 (22);

[0086] Wherein: and are respectively the upper and lower limits of the output of the conventional unit; and are respectively the ramping rate and the sliding rate of the conventional unit; Δt is the duration of a single time period; is the maximum load power of the load node, and the constraint condition (12) limits the load shedding amount within a single time period; is the predicted output of the wind-solar power plant, is the predicted output of the photovoltaic power plant, and the constraints (13) and (14) realize the output dispatching of the wind farm by controlling the grid-connected wind-solar units; and are 0-1 variables, representing the charging and discharging states of the energy storage at node i at time t respectively; is the maximum charging and discharging power; is the stored electricity capacity of the energy storage at node i at time t; is the maximum electricity that the energy storage can store; is the self-discharge rate of the energy storage; and are respectively the charging and discharging efficiencies of the energy storage; is the maximum number of charging and discharging times of the energy storage during the preventive network reconstruction period; l is the number of branches of the distribution system; L is the set of network branches; β ij is a 0-1 variable, taking 1 when node i is the parent node of node j, otherwise taking 0; N is the number of nodes in the system.

[0087] In these embodiments, based on minimizing the dispatching cost of the distribution network in the preventive stage, the objective function of the preventive stage is constructed, and according to the source-network-load-storage information, multiple constraints in the preventive stage are established, which can enable the system to actively respond to upcoming extreme events and avoid a large number of power outages caused by insufficient system preparation.

[0088] In some embodiments of these embodiments, the extreme event model in the middle layer of the emergency stage simulates the disturbance behavior of the distribution network under extreme events.

[0089] Exemplarily, extreme uncertainty events include hurricane weather events. Modeling hurricane weather events includes:

[0090] Modeling based on the Batts wind field model (full name: Boundary Layer Wind Tunnel Model), the wind speed near the generator set is defined as a function of its distance from the hurricane eye and the maximum sustained wind speed, and can be expressed by the following formula (23):

[0091]

[0092] In the formula, represents the wind speed near the generator set; r wind and R M respectively represent the distances from the hurricane eye to the generator set and the maximum wind speed point; η wind is a model parameter related to the attenuation of hurricane intensity, varying between 0.5 and 0.7; represents the maximum hurricane speed and can be expressed by the following formula (24):

[0093]

[0094] In the formula, is the moving speed of the hurricane; is the gradient wind speed. The gradient wind speed determined by the voltage difference of the hurricane eye and the distance from the hurricane eye to the maximum wind speed point can be expressed by the following formula (25):

[0095]

[0096] In the formula, H wind is an empirical coefficient, is the Coriolis parameter. Ω is the angular velocity of the Earth's rotation, and φ is the hurricane latitude. Δp wind can be expressed as the following formula (26):

[0097]

[0098] In the formula, is the air pressure of the hurricane eye, is the average sea level air pressure. The distance R M from the hurricane eye to the maximum wind speed point wind is related to the function of Δp

[0099] R M = exp(-0.1239Δ(p wind )) 0.6003 + 5.1034) (27);

[0100] In the formula, the exp function is the exponential function.

[0101] Concrete poles are severely affected by hurricane wind damage, and the overhead lines are negligibly affected by hurricanes. Wind loads act on the elevation through hurricanes, which can cause the pole body to collapse, especially at the root of the pole body. Considering three types of wind loads, namely the wind load on the transmission line, the wind load on the concrete pole, and the wind load on the insulator, the three types of wind loads can be expressed by the following formula (28):

[0102]

[0103] Where ω line is the wind load on the transmission line; ω pole is the wind load on the concrete pole; ω insulator is the wind load on the insulator; θ is the angle between the conductor and the wind direction; l H is the horizontal span of the distribution line; α wind is the unevenness coefficient of wind pressure; μ z is the coefficient of variation of wind pressure with height, μ s , μ sc are the shape coefficients of the concrete pole and the line respectively, β wind is the wind vibration coefficient; is the number of insulator strings for a single-phase line, is the number of insulators per string; is the windward area of each insulator; A wind is the windward projected area of the member.

[0104] The member also satisfies the following formula (29):

[0105]

[0106] Where M x is the bending moment on the pole, is the height of the pole, is the distance from the pole beam to the root of the pole body; is the projected area of the pole body; D0, D x are the pole body diameter and the pole body root diameter respectively, m x is the additional bending moment; w li is the wind load acting on the cross arm or the top of the pole; w q is the wind load acting on the pole body (concrete pole); ω li is the wind load intensity of the insulator; ω q is the wind load intensity of the pole body; is the reference height for wind load calculation.

[0107] The bending moment can be modeled as a normal distribution and can be expressed by the following formula (30):

[0108]

[0109] Wherein, μ q and δ q are respectively the mean value and the standard variance of the flexural strength M of the rod body, which can be obtained from empirical data. The state of the extreme point can be expressed by the following formula (31): q If the electric pole is in a fault state, the distribution line connected to the electric pole may be out of service. The damage probability of the rod body supporting the line conforms to the vulnerability curve and can typically be modeled as a normal cumulative distribution function, which is expressed by the following formula (32):

[0110]

[0111] Wherein, pr is the damage probability of the rod body supporting the line.

[0112]

[0113]

[0114] The outage probability of line ij can be expressed by the following formula (33):

[0115]

[0116] Wherein, (pr) ij represents the outage probability of line ij; Q represents the total number of electric pole bodies on line ij; (pr) q is the damage probability of the rod body q supporting the line.

[0117] Considering the uncertain power line interruption caused by the hurricane, the fuzzy set can be expressed by the following formula (34):

[0118]

[0119] Wherein, G is the fuzzy set; the first row of the fuzzy set G represents that the interruption probability of line ij is less than (pr) ij , and the first-order moment information can be obtained from the component vulnerability model induced by the hurricane; the second row constrains the expected number of damaged lines k d , indicating that considering N-k d faults, no more than k d lines are out of service simultaneously. P(Z) represents the set of all probability distributions on Z and is the set of all distribution lines based on the N-K criterion. The negative impact degree of extreme events can be characterized by the proportion of the number of damaged lines in all power lines.

[0120] In these embodiments, by modeling the hurricane weather event, extreme events can be simulated and used to simulate the impact of extreme event data on the distribution network during the emergency phase.

[0121] ​In some embodiments of these embodiments, for the emergency network reconstruction model of the inner layer in the emergency stage, based on minimizing the scheduling cost of the distribution network in the prevention stage and minimizing the load switching cost of the distribution network on the premise of maximizing the negative impact degree of extreme events in the emergency stage, the preventive network reconstruction model and the optimization model are solved nestedly to obtain the first scheduling strategy in the prevention stage and the second scheduling strategy in the emergency stage for the dispatchable resources, and further include:

[0122] First, based on maximizing the negative impact degree of extreme events, determine the uncertainty constraints regarding extreme events according to the dispatchable resource information.

[0123] Second, based on minimizing the load switching cost of the distribution network in the emergency stage, determine the second objective function in the emergency stage according to the dispatchable resource information; the second objective function includes uncertainty constraints.

[0124] Specifically, with the evolution and development of extreme events, the intermediate disturbance layer models the severe scenarios that the system may suffer in the form of a line time-sequence fault set and a new energy output uncertainty set, as the scenario basis for subsequent system emergency network reconstruction decisions. As the second line of defense of the power system, the emergency network reconstruction of the system in severe disturbance scenarios ensures the power supply safety of more loads and reduces the losses caused by large power outages. To ensure power balance in the power grid in an emergency, the dispatchable resources in the emergency stage mainly include: new energy units, energy storage charging and discharging, transmission line switches, and emergency load shedding. The obtained dispatchable resource information includes the source-network-load-storage information of the above dispatchable resources. Considering minimizing the load switching cost of the distribution network dispatch resources in the optimization objective, the second objective function can be expressed by the following formula (35):

[0125]

[0126] where T Eme is the adjustment time in the emergency network reconstruction stage, and N, W, PV, and L are the sets of network nodes, wind power, photovoltaic units, and network branches respectively; is the curtailed wind and photovoltaic power in the emergency stage; is the load shedding amount of node j in the emergency stage; is the unit cost of curtailed wind in the emergency stage; is the unit cost of curtailed light in the emergency stage; represents the unit reduction cost of the load in the emergency stage; C switch represents the switching cost in the emergency stage; z ij is the 0-1 variable of the line switch in the emergency stage, and z ij,0 is the initial state of the line in the emergency stage.

[0127] The above second objective function includes second linearized DistFlow branch model constraints and second other constraints, where the second linearized DistFlow branch model constraints include uncertainty constraints.

[0128] Regarding the second linearized DistFlow branch model constraints, the uncertainty constraints can directly incorporate the uncertainty of line outages into the decision-making process, making the network reconstruction more adaptable to changes, capable of quickly responding to unexpected outages, mitigating the impact of extreme events, optimizing resource utilization while ensuring the stability and security of the power grid, and improving the overall resilience of the system. The second linearized DistFlow branch model constraints including uncertainty constraints can be expressed by the following formulas (36)-(42):

[0129]

[0130]

[0131] In the formulas: are the active and reactive power flows of line ij at time t respectively; κ Eme is the confidence level, z ij,t is the 0-1 variable of the line status; inf represents the maximum lower bound; the uncertainty constraints introduced by p in formulas (38)-(42) include the constraints of power line interruptions caused by extreme events.

[0132] Regarding the second other constraints, they include wind and solar power output uncertainty constraints, energy storage constraints, and load shedding constraints. They can be expressed by the following formulas (43)-(57):

[0133]

[0134]

[0135] β ij +β ji =z ij (55);

[0136]

[0137] β 1j =0 (57);

[0138] In the formulas, is a 0-1 variable used to indicate whether the output of the wind and solar units fluctuates within this time interval. The boundary points of the output uncertainty set represent the situation of the largest output fluctuation, that is, the worst case of the wind and solar power output is included in the uncertainty set; η w,t and η w,s , η pv,t and η pv,sThey respectively represent the uncertainty budget values of wind and light output in terms of time and space, which respectively depend on the total number of simulation cycles and the number of grid-connected power plants; Δt is the duration of a single time period; is the maximum load power of the load node, and the constraint condition (12) limits the load shedding amount within a single time period; is the predicted output of the wind and light power plant, is the predicted output of the photovoltaic power plant; and are 0-1 variables, representing the charging and discharging states of the energy storage at node i at time t respectively; is the maximum charging and discharging power; is the electricity storage capacity of the energy storage at node i at time t; is the maximum amount of electricity that the energy storage can store; is the self-discharge rate of the energy storage; and are the charging and discharging efficiencies of the energy storage respectively; is the maximum number of charging and discharging times of the energy storage during the preventive network reconstruction period; l is the number of branches in the distribution system; L is the set of branches in the distribution system; β ij is a 0-1 variable, taking 1 when node i is the parent node of node j, otherwise taking 0; N is the number of nodes in the system.

[0139] In these embodiments, by minimizing the load switching cost of the distribution network based on the emergency stage, an emergency network reconstruction model including a second objective function is established, which ensures the power supply safety of more loads and reduces the losses caused by large power outages under severe disturbance scenarios of the system.

[0140] In some embodiments of these embodiments, on the premise of minimizing the scheduling cost of the distribution network in the preventive stage and maximizing the negative impact degree of extreme events in the emergency stage, the load switching cost of the distribution network is minimized, and the preventive network reconstruction model and the optimization model are solved by nesting to obtain the first scheduling strategy in the preventive stage and the second scheduling strategy in the emergency stage for the dispatchable resources, further including:

[0141] First, determine the master problem according to the first objective function in the preventive stage.

[0142] Second, determine the sub-problem according to the second objective function in the emergency stage.

[0143] Third, based on the nested solution algorithm, solve the master problem and the sub-problem by nesting to obtain the first scheduling strategy in the preventive stage and the second scheduling strategy in the emergency stage for the dispatchable resources.

[0144] Specifically, the nested solution algorithm can be based on the nested column constraint generation algorithm.

[0145] The determined main problem is to solve the first objective function. When solving the main problem for the first time, the uncertainty set can be set as an empty set, that is, the distribution network is not affected by extreme events. As the number of iterations increases, new worst-case scenarios are continuously added to the uncertainty set. Then, variables corresponding to the worst-case scenarios are added to the main problem, and the optimal first scheduling strategy in the prevention stage is obtained by solving the model. Let \(i\) represent the current iteration number, and the iterative main problem can be written in the compact form shown in Equation (58) below:

[0146]

[0147] where, and represent the worst operating conditions of the \(k\)-th iteration optimization problem; \(x 2,(k) represents the optimization variable added to the main problem in the \(k\)-th iteration; \(\rho\) is an auxiliary variable used to represent the objective function value of the emergency network reconstruction stage to be optimized.

[0148] The determined sub-problem is to solve the second objective function, and its general form can be simplified to the following Equations (59)-(61):

[0149] min \(c T x_2\) (59);

[0150]

[0151] x_2\in\chi\) (61);

[0152] where, \(\chi\) is the feasible set of decision variables; \(w 0 (x_2)\) are the known and determinable system characteristics or operating conditions related to the decision variable \(x_2\); \(z\) is a random variable representing equipment failures or weather factors in the system; \(\varepsilon\) is the upper limit of the default probability or failure probability, that is, the risk level. Equations (35)-(57) can be expressed as (59)-(61). However, the risk-opportunity constraints in Equations (38)-(41) are still difficult to solve, and an easy-to-handle approximation method based on moment-based ambiguity sets for distributionally robust chance constraints (full name: Distributionally Robust Chance Constraint, abbreviation: DRCC) can be developed. The risk-opportunity constraint (60) applying distributionally robust optimization in the risk measure method combining distributionally robust optimization and conditional value-at-risk (full name: Worst-Case Conditional Value-at-Risk, abbreviation: WC-CVaR) can be approximated to the general form, specifically as Equation (62):

[0153]

[0154] where CVaR ε is the CVaR function at level ε with respect to the probability distribution p; the CVaR at level ε with respect to the probability distribution P is defined by the following formula (63):

[0155]

[0156] where (·) + = Max{·, 0}, and Max is the maximum value therein; the interchange of sup and inf is based on the stochastic saddle point theorem; the inner worst-case expectation problem (55) can be equivalently expanded into formula (64):

[0157]

[0158] Therefore, combining the above derivations with formulas (59) and (61) makes the emergency stage described as a maximization-minimization problem, that is, as the middle layer and the inner layer of the two-stage three-layer optimization. The middle layer of the model considers the impacts of "attacker" behaviors such as line outages and uncertainties in wind and photovoltaic power outputs, and finds the most severe disturbance scenario as the scenario basis for the inner-layer emergency network reconstruction; the emergency network reconstruction in the inner layer of the model is based on the operating state under the preventive network reconstruction strategy to cope with the severe scenario, so as to ensure the minimum power outage loss of the system; based on the outer-layer preventive network reconstruction strategy, the middle layer and the inner layer determine the worst scenario through a game, and then transfer the worst scenario to the outer layer to update the preventive network reconstruction strategy. The inner-layer problem can be transformed into a maximization problem by using Lagrangian duality, and the two-layer problem is combined into a single-layer problem for solution, and it is ensured that the emergency network reconstruction problem and its dual problem have the same optimal solution under the strong duality condition. The sub-problem after dual transformation is as formula (65):

[0159]

[0160] where λ is the dual variable of the constraint conditions in the emergency stage; the transformed model contains bilinear terms where z is a 0-1 integer variable; z η is the set of all 0-1 variables z of the set η; are the uncertainty variables of wind power and photovoltaic power under the preventive stage; z l is the 0-1 variable of the transmission line fault outage.

[0161] In these embodiments, by respectively transforming the objective functions of the preventive stage and the emergency stage into the main problem and the sub-problem, and performing dual transformation on them, the two-stage three-layer problem is transformed into a bilinear solution problem.

[0162] In some embodiments of these embodiments, based on the nested solution algorithm, the master problem and the sub-problem are solved nestedly to obtain the first scheduling strategy for the preventive stage of schedulable resources and the second scheduling strategy for the emergency stage, including:

[0163] Repeat the following steps until the preset conditions are met:

[0164] First, solve the master problem to obtain the first scheduling result for the schedulable resources.

[0165] Second, in response to the first scheduling result not meeting the preset conditions, substitute the first scheduling result into the sub-problem, solve the sub-problem, and obtain the second scheduling result for the schedulable resources.

[0166] Third, based on the first scheduling result and the second scheduling result, adjust the parameters of the master problem and the sub-problem respectively to obtain the updated master problem and sub-problem.

[0167] Among them, it is judged whether the preset conditions are met according to the first scheduling result.

[0168] Specifically, for step one, first initialize the parameters of the master problem: the upper bound value U of the optimal objective value B , the lower bound value L B , and the convergence threshold δ for judging convergence max . The upper bound value U B , the lower bound value L B are respectively represented by formulas (66) and (67):

[0169]

[0170] Secondly, solve formula (57) to obtain x1 and ρ.

[0171] For step two, substitute x1 and ρ into formula (65) to solve the sub-problem, and obtain the optimal objective value ε and the control parameter z of the worst-case scenario regarding the transmission line l and the control parameter z of the uncertainty variable w .

[0172] For step three, update the lower bound value L according to ρ of the first scheduling result and formula (66) B ; when the worst-case scenario parameters are z l and z w , add perturbation parameters according to the extreme event modeling to update the parameters, and substitute the added perturbation parameters into the master problem to update the upper bound value U B and the lower bound value L B .

[0173] The preset condition is to judge the upper bound value U B and the lower bound value L BWhether the absolute value of the difference is less than the convergence threshold δ max .

[0174] Among them, the final solution in the prevention stage is the first scheduling result obtained by solving the master problem in the last iteration; the final solution in the emergency stage is the second scheduling result obtained by solving the sub-problem in the last iteration. According to the first scheduling result and the second scheduling result obtained in the last iteration, formulate the first scheduling strategy in the prevention stage and the second scheduling strategy in the emergency stage of the schedulable resources.

[0175] In these embodiments, by nestedly solving the master problem and the sub-problem, the determined parameters of each schedulable resource in the prevention stage and the emergency stage are obtained, and thus the first scheduling strategy in the prevention stage and the second scheduling strategy in the emergency stage are obtained.

[0176] Figure 2 The structure diagram of the distribution network resource scheduling device provided by this application is as Figure 2 shown. The distribution network resource scheduling device 20 provided in this embodiment includes:

[0177] An acquisition module 201, configured to acquire schedulable resource information of the distribution network.

[0178] A construction module 202, configured to construct a two-stage model based on the schedulable resource information. The two-stage model includes a preventive network reconstruction model in the prevention stage and an optimization model in the emergency stage; among them, the optimization model in the emergency stage includes an extreme event model and an emergency network reconstruction model.

[0179] A solution module 203, configured to perform nested solution on the preventive network reconstruction model and the optimization model based on minimizing the scheduling cost of the distribution network in the prevention stage and minimizing the load switching cost of the distribution network on the premise of maximizing the negative impact degree of extreme events in the emergency stage, so as to obtain the first scheduling strategy in the prevention stage and the second scheduling strategy in the emergency stage of the schedulable resources.

[0180] A scheduling module 204, configured to schedule the schedulable resources according to the first scheduling strategy in the prevention stage or the second scheduling strategy in the emergency stage.

[0181] In a possible implementation manner, the solution module 203 is further configured to determine the first objective function in the prevention stage based on minimizing the scheduling cost of the distribution network in the prevention stage and according to the schedulable resource information; among them, the first objective function includes the output constraint of the schedulable resources determined according to the schedulable resource information.

[0182] In a possible implementation manner, the solution module 203 is further configured to determine the uncertainty constraint regarding extreme events according to the schedulable resource information based on maximizing the negative impact degree of extreme events;

[0183] Based on minimizing the load switching cost of the distribution network during the emergency stage, and according to the dispatchable resource information, determine the second objective function during the emergency stage; the second objective function includes uncertainty constraints.

[0184] In a possible implementation manner, the uncertainty constraints include the constraints of power line outages caused by extreme events.

[0185] In a possible implementation manner, the solving module 203 is further configured to determine the master problem according to the first objective function in the prevention stage;

[0186] Determine the sub-problem according to the second objective function in the emergency stage;

[0187] Based on the nested solving algorithm, perform nested solving on the master problem and the sub-problem to obtain the first scheduling strategy in the prevention stage and the second scheduling strategy in the emergency stage for the dispatchable resources.

[0188] In a possible implementation manner, the solving module 203 is further configured to repeatedly execute the following steps until the preset conditions are met:

[0189] Solve the master problem to obtain the first scheduling result for the dispatchable resources;

[0190] In response to the first scheduling result not meeting the preset conditions, substitute the first scheduling result into the sub-problem, solve the sub-problem, and obtain the second scheduling result for the dispatchable resources;

[0191] Based on the first scheduling result and the second scheduling result, respectively adjust the parameters of the master problem and the sub-problem to obtain the updated master problem and sub-problem; where

[0192] Judge whether the first scheduling result meets the preset conditions.

[0193] In a possible implementation manner, the dispatchable resource information includes at least one of the following source-network-load-storage information of dispatchable resources: new energy units, conventional units, transmission line switches, and load shedding devices.

[0194] The distribution network resource scheduling device provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effects are similar, which will not be elaborated here in this embodiment.

[0195] Figure 3 It is a schematic structural diagram of the distribution network resource scheduling device provided in this application. As Figure 3 shown, the electronic device 30 provided in this embodiment includes: at least one processor 301 and a memory 302. Optionally, the device 30 further includes a communication component 303. Among them, the processor 301, the memory 302, and the communication component 303 are connected through a bus 304.

[0196] In a specific implementation process, at least one processor 301 executes computer-executable instructions stored in a memory 302, such that at least one processor 301 executes the above-described method.

[0197] For the specific implementation process of the processor 301, reference may be made to the above method embodiments. Their implementation principles and technical effects are similar, and thus will not be elaborated herein.

[0198] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU for short), or may also be other general-purpose processors, digital signal processors (DSP for short), application specific integrated circuits (ASIC for short), etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in combination with the invention may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor.

[0199] The memory may include a high-speed random access memory (RAM), and may also include non-volatile memory (NVM), such as at least one disk memory.

[0200] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the buses in the drawings of the present application are not limited to only one bus or one type of bus.

[0201] The present application also provides a computer program product, including a computer program which, when executed by a processor, implements the above-described method.

[0202] The present application also provides a computer-readable storage medium storing computer-executable instructions which, when executed by a processor, implement the above-described method.

[0203] The above-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium may be any available medium accessible by a general-purpose or special-purpose computer.

[0204] An exemplary readable storage medium is coupled to the processor, enabling the processor to read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium may also be a component of the processor. The processor and the readable storage medium may be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium may also exist as discrete components in a device.

[0205] The division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other may be indirect couplings or communication connections through some interfaces, devices or units, and may be in electrical, mechanical or other forms.

[0206] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0207] In addition, the functional units in various embodiments of the present invention may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0208] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., all kinds of media that can store program codes.

[0209] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When this program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes: ROMs, RAMs, magnetic disks, or optical discs, etc., all kinds of media that can store program codes.

[0210] Finally, it should be noted that: After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily think of other implementation manners of the present invention. The present invention is intended to cover any variations, uses, or adaptations of the present invention, which follow the general principles of the present invention and include common general knowledge or conventional technical means in the technical field not disclosed in the present invention. It is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A power distribution network resource scheduling method, characterized in that Including: Obtaining the dispatchable resource information of the distribution network; Based on the dispatchable resource information, constructing a two-stage model, where the two-stage model includes a preventive network reconstruction model in the preventive stage and an optimization model in the emergency stage; among them, the optimization model in the emergency stage includes an extreme event model and an emergency network reconstruction model; Based on minimizing the dispatch cost of the distribution network in the preventive stage and minimizing the load switching cost of the distribution network on the premise of maximizing the negative impact degree of extreme events in the emergency stage, nestedly solving the preventive network reconstruction model and the optimization model to obtain the first dispatch strategy in the preventive stage and the second dispatch strategy in the emergency stage for the dispatchable resources; Dispatching the dispatchable resources according to the first dispatch strategy in the preventive stage or the second dispatch strategy in the emergency stage.

2. The method according to claim 1, characterized in that The nested solution of the preventive network reconstruction model and the optimization model based on minimizing the dispatch cost of the distribution network in the preventive stage and minimizing the load switching cost of the distribution network on the premise of maximizing the negative impact degree of extreme events in the emergency stage to obtain the first dispatch strategy in the preventive stage and the second dispatch strategy in the emergency stage for the dispatchable resources includes: Based on minimizing the dispatch cost of the distribution network in the preventive stage, determining the first objective function in the preventive stage according to the dispatchable resource information; among them, the first objective function includes the output constraints of the dispatchable resources determined according to the dispatchable resource information.

3. The method according to claim 2, wherein The nested solution of the preventive network reconstruction model and the optimization model based on minimizing the dispatch cost of the distribution network in the preventive stage and minimizing the load switching cost of the distribution network on the premise of maximizing the negative impact degree of extreme events in the emergency stage to obtain the first dispatch strategy in the preventive stage and the second dispatch strategy in the emergency stage for the dispatchable resources further includes: Based on maximizing the negative impact degree of extreme events, determining the uncertainty constraints regarding the extreme events according to the dispatchable resource information; Based on minimizing the load switching cost of the distribution network in the emergency stage, determining the second objective function in the emergency stage according to the dispatchable resource information; the second objective function includes the uncertainty constraints.

4. The method according to claim 3, wherein The uncertainty constraints include the constraints of power line interruptions caused by extreme events.

5. The method according to claim 3, wherein The nested solution of the preventive network reconstruction model and the optimization model based on minimizing the dispatch cost of the distribution network in the preventive stage and minimizing the load switching cost of the distribution network on the premise of maximizing the negative impact degree of extreme events in the emergency stage to obtain the first dispatch strategy in the preventive stage and the second dispatch strategy in the emergency stage for the dispatchable resources further includes: Determining the master problem according to the first objective function in the preventive stage; Determining the sub-problem according to the second objective function in the emergency stage. Based on the nested solution algorithm, the master problem and the sub-problem are solved nestedly to obtain the first scheduling strategy for the preventive stage and the second scheduling strategy for the emergency stage of the schedulable resources.

6. The method according to claim 5, wherein The nested solution of the master problem and the sub-problem based on the nested solution algorithm to obtain the first scheduling strategy for the preventive stage and the second scheduling strategy for the emergency stage of the schedulable resources includes: Repeatedly execute the following steps until a preset condition is met: Solve the master problem to obtain the first scheduling result for the schedulable resources; In response to the first scheduling result not meeting the preset condition, substitute the first scheduling result into the sub-problem and solve the sub-problem to obtain the second scheduling result for the schedulable resources; Based on the first scheduling result and the second scheduling result, adjust the parameters of the master problem and the sub-problem respectively to obtain the updated master problem and sub-problem; where Judge whether the preset condition is met according to the first scheduling result.

7. The method according to any one of claims 1-6, characterized in that, The schedulable resource information includes the source-network-load-storage information of at least one of the following schedulable resources: new energy units, conventional units, transmission line switches, and load shedding devices.

8. A distribution network resource scheduling device, characterized in that It includes: An acquisition module for acquiring the schedulable resource information of the distribution network; A construction module for constructing a two-stage model based on the schedulable resource information, where the two-stage model includes a preventive network reconstruction model for the preventive stage and an optimization model for the emergency stage; where the optimization model for the emergency stage includes an extreme event model and an emergency network reconstruction model; A solution module for nestedly solving the preventive network reconstruction model and the optimization model based on minimizing the scheduling cost of the distribution network in the preventive stage and minimizing the load switching cost of the distribution network on the premise of maximizing the negative impact degree of extreme events in the emergency stage, to obtain the first scheduling strategy for the preventive stage and the second scheduling strategy for the emergency stage of the schedulable resources; A scheduling module for scheduling the schedulable resources according to the first scheduling strategy for the preventive stage or the second scheduling strategy for the emergency stage.

9. A distribution network resource scheduling device, characterized in that, It includes: A memory, a processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory, so that the processor executes the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, Computer execution instructions are stored in the computer-readable storage medium, and when the computer execution instructions are executed by the processor, they are used to implement the method according to any one of claims 1-7.

11. A computer program product, characterized in that, It includes a computer program, and when the computer program is executed by the processor, it implements the method according to any one of claims 1-7.

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